AWS cloud data warehouse with SQL-based machine learning built in
Amazon Redshift is AWS's petabyte-scale data warehouse. It supports columnar storage, massively parallel query execution, workload management for mixed concurrency and Spectrum queries that reach data in S3 without loading it first. Redshift ML lets analysts create models using SQL, with SageMaker handling training behind the scenes, and provides prediction functions callable from queries. Both provisioned clusters and a serverless option are available, so teams can choose between reserved capacity and pay-per-use. It is a mature warehouse first and an AI feature set second, which is the right way to evaluate it.
Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.
large-scale warehousing, bi backends, etl consolidation and sql-based prediction
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Redshift suits repeated, complex, high-concurrency workloads where consistent performance justifies provisioned capacity. Athena suits occasional queries over files already in S3 with no infrastructure. Many organisations use both and keep the expensive queries on Redshift.
It is a convenience layer: you write SQL to create a model and SageMaker trains it, then you call a prediction function. It covers common tabular problems well, but serious model development still happens in a dedicated ML environment.
Node hours or serverless compute, storage, and the data scanned by Spectrum queries. Idle clusters and unpartitioned S3 scans are the two most common sources of avoidable cost.
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